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Doctorate in Computer Science and Information Systems (PhD), University of Colorado Denver Business School, CU Denver

Business School, University of Colorado Denver
 


Take the PhD courses offered by your unit (CS or IS) and one PhD course from the other unit. You may take PhD courses from the other unit to fulfill the breadth requirement.

 

Advances In Management Information Systems, ISMG 7200

  • Instructor: Ping Walter
  • Last offering: Fall 2012
  • Next offering: Fall 2013
  • Description

The objective of this course is to introduce doctoral students to various research methods, both quantitative and qualitative. This course is not meant for students to become experts in any specific method — to accomplish this, you’ll need to engage in a major study using that research method — however, students are expected to become educated consumers of research articles after completing this course and to be able to start the first steps towards conducting academic research. Later on in doctoral studies, through a second paper and a dissertation, a student will fine tune a specific research method.

Sample Syllabus

 

Analytical Research in Information Systems, ISMG 7210

  • Instructor: Michael Mannino
  • Last offering: Spring 2013
  • Next offering: Spring 2015
  • Description

This course covers methodology, background, and analytical research topics of interest to the information systems research community. The methodology background includes basic classification measures, confusion matrices, ROC curves, resampling, experiment design, and cost measures. Research topic areas covered include evaluation measures for classifier performance, cost based learning, statistical methods for classifier evaluation, sequential decision models, deception detection, recommendation systems, and data mining usage in internal auditing. The goals of the course are to (1) provide exposure to topic areas and methods of analytical information systems research, (2) critically analyze research articles, (3) develop depth knowledge and research method skills in an analytical research area through a data analysis project, and (4) improve scientific writing skills.

Sample Syllabus

 

Topics in Behavioral and Organizational Research in Management Information Systems, ISMG 7211

  • Instructor: Judy Scott
  • Last offering: Fall 2012
  • Next offering: Fall 2014
  • Description

The objective of this course is to provide in depth exposure to some key behavioral, managerial, and organizational theories and models used in Information Systems research. This course emphasizes recent IS research articles from the most prestigious IS journals for each topic. With an introduction to recent controversial topics and opinions expressed by leading IS researchers on (1) the identity of the IS discipline, and on (2) rigor and relevance, we move on to social theories, trust, organizational transformation, organizational learning and knowledge management, resource-based, innovation and coordination theories. Reading and critiquing several exemplary articles facilitate students learning of how these theories and models have been applied to the topics IT in Healthcare and ERP implementation. By the end of the semester, students are expected to identify an interesting research area, in which they can use the theories and models in this course, and to provide a problem statement, literature review, and a research plan. The evaluation of the student’s report will give significant emphasis to research-oriented details.

Sample Syllabus

 

Research Methods: Design and Analysis, ISMG 7220

  • Instructor: Ron Ramirez
  • Last offering: Spring 2012
  • Next offering: Spring 2014
  • Description

Structural equation modeling (SEM) is a method gaining wide acceptance in IT-based management research. Based on recommendations of IT department professors, the course now concentrates on SEM specifically. The class will focus on providing a working knowledge of SEM through the completion of three general topics. First, we will start with the basics of social science research method concepts. Second, we will cover SEM foundational concepts. Third and finally, we will concentrate on PLS estimation techniques. The benefits of these three elements of the course will be a foundation of conceptual and theoretical knowledge that can be used to complete journal quality empirical research.

During the third stage of the course, we will gain hands-on experience with PLS estimation using R statistical software. This open source software is becoming a widely adopted tool within empirical IT research. The benefit of this course component will be the development of a software skill set that you will be able to contribute to future collaborative research teams.

A central component of the class will be current readings in IT research. These articles will provide a context for the three elements discussed above. The benefits of this arduous class component will be the development of current knowledge in IT research, exposure to research in the IT area where SEM is applicable, and the creation of a potential starting point and reference of research topics you could pursue in your PhD program at UCD.

After completing this class, you may not be an expert on research methods, the SEM technique, or on the hands-on application of SEM yourself. However, through the completion of the course you will gain a working knowledge of these three items, providing a starting point for research as you advance in your PhD studies and your career.

Sample Syllabus


Topics in Network Computing, CSCI 7799

  • Instructor: Ilkyeun Ra
  • Last offering: Fall 2012
  • Next offering: Fall 2014
  • Description

In this course, students will investigate the current technical issues of grid computing by reading the required textbook as well as several assigned technical papers. Each student will write a survey paper and complete a term project on one of the topics provided by the instructor. Course will consist of lectures and student class presentations (seminars). After this course, each student should understand, at a deep level, several specific hardware and software tradeoffs for application performance, development, and management on a network computing technologies including grid/cluster, cloud, p2p, and pervasive computing.​

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